{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construction of mixed-dimensional grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will show:\n",
    "\n",
    "1. How to define fractures and a fracture network in 2d and 3d domains.\n",
    "2. How to construct a family of meshes that represent the domain, the fractures and their intersections.\n",
    "3. Assembly of the grids into a `MixedDimensionalGrid` container that stores all grids and the geometric and topological relation between them.\n",
    "\n",
    "Together, these are the first steps towards creating a simulation model for a mixed-dimensional problem in fractured domains.\n",
    "For most simulation purposes, the final mixed-dimensional grid is all that is needed. Therefore, we start by showing a shortcut for obtaining a `MixedDimensionalGrid` given a set of fractures, a domain and mesh size parameters. All these will be described in more detail below.\n",
    "We also mention that access to methods generating mixed-dimensional grids for a small selection of predefined fracture networks is available under `pp.applications.md_grids`.\n",
    "\n",
    "The representation of a single fracture is handled by the `Fracture` class. Combining several fractures into a network is then handled by one of the classes `FractureNetwork2d` or `FractureNetwork3d`, depending on the dimension of the domain.\n",
    "\n",
    "## Meshing of 2d fractures\n",
    "A two-dimensional fracture is formed by specifying the coordinates of its two endpoints. These coordinates are collected into an array such that each column contains the coordinates of a specific point. The fractures are instantiated by the `LineFracture` class, which inherits from the `Fracture` class.\n",
    "\n",
    "Below we define two fractures, one extending from (0,0) to (2,0) and one from (1,0) to (1,1). We then combine the geometry information into a fracture network by calling the method `create_fracture_network()`. This method in turn instantiates an object of the class `FractureNetwork2d`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import porepy as pp\n",
    "\n",
    "# Define a rectangular domain in terms of range in the two dimensions\n",
    "bounding_box = {'xmin': -2, 'xmax': 3, 'ymin': -2, 'ymax': 3}\n",
    "domain = pp.Domain(bounding_box=bounding_box)\n",
    "\n",
    "# Define each individual fracture, collect into a list.\n",
    "frac1 = pp.LineFracture(np.array([[0, 2],\n",
    "                                  [0, 0]]))\n",
    "frac2 = pp.LineFracture(np.array([[1, 1],\n",
    "                                  [0, 1]]))\n",
    "fractures = [frac1, frac2]\n",
    "\n",
    "# Define a fracture network in 2d\n",
    "network_2d = pp.create_fracture_network(fractures, domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed by producing the fracture and matrix meshes, which are collected in a mixed-dimensional grid. This is done by calling the method `create_mdg()`, with the following input: The type of grid, meshing arguments and the fracture network. The different options of grid types are `\"simplex\"`, `\"cartesian\"` and `\"tensor_grid\"`. Various meshing arguments can be provided and are contained in a dictionary. Note that different grid types have different meshing arguments. In this case, we provide a value of an overall target cell size, as well as a smaller target cell size near the fractures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Set overall target cell size and target cell size close to the fracture.\n",
    "mesh_args: dict[str, float] = {\"cell_size\": 1.0, \"cell_size_fracture\": 0.3}\n",
    "\n",
    "# Generate a mixed-dimensional grid\n",
    "mdg = pp.create_mdg(\"simplex\", mesh_args, network_2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the network, we see that the two fractures form a Y (or T) type intersection. The intersection point is identified during meshing and assigned a zero-dimensional mesh. The plot of `mdg` shows how the chosen mesh arguments result in refinement towards the fractures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_2d.plot()\n",
    "pp.plot_grid(mdg, figsize=(8,8), plot_2d=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meshing of 3d fractures\n",
    "Fractures in 3d are polygons instead of lines. They are defined much in the same way as for 2d fractures, namely by specifying the coordinates of the vertices of the polygon. Instantiation of the fractures is done by the `PlaneFracture` class, which once again inherits from the `Fracture` class. Note that when defining the mesh we are now specifying a minimum cell size, as well as a target cell size close to the external boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# The fractures are specified by their vertices, which are 3 x num_pts arrays\n",
    "f_1 = pp.PlaneFracture(np.array([[0, 2, 2.5, 0],\n",
    "                                 [0, 0, 1, 1],\n",
    "                                 [0, 0, 1, 1]]))\n",
    "f_2 = pp.PlaneFracture(np.array([[1, 1, 1, 1],\n",
    "                                 [-1, 2, 2, -1],\n",
    "                                 [-1, -1, 2, 2]]))\n",
    "fractures = [f_1, f_2]\n",
    "\n",
    "# Also define the domain\n",
    "bounding_box = {'xmin': -1, 'xmax': 3, 'ymin': -2, 'ymax': 3, 'zmin': -1.5, 'zmax': 3}\n",
    "domain = pp.Domain(bounding_box=bounding_box)\n",
    "\n",
    "# Define a 3d FractureNetwork, similar to the 2d one\n",
    "network = pp.create_fracture_network(fractures, domain)\n",
    "\n",
    "# Generate the mixed-dimensional mesh\n",
    "mesh_args = {'cell_size_boundary': 1.0, 'cell_size_fracture': 0.5, 'cell_size_min': 0.2}\n",
    "mdg = pp.create_mdg(\"simplex\", mesh_args, network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import filters \n",
    "FractureNetworks (2d and 3d) can also be defined directly from files storing their data, see `pp.fracture_importer` for details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of the mixed-dimensional grid\n",
    "The set of meshes in the `MixedDimensionalGrid` can be exported for simulation by writing a .vtu file.\n",
    "Then it can be visualized using, for instance, ParaView."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp.Exporter(mdg, 'mixed_dimensional_grid').write_vtu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using some functionality within ParaView, we can see how the grids on fracture surfaces intersect with each other and with the matrix grid.\n",
    "\n",
    "<img src='img/mixed_dimensional_grid.png'  width=600>\n",
    "\n",
    "Note that in both of our examples of fracture networks we defined the bounding box to not intersect with the fractures. If the domain would have been smaller, fractures that intersect a face of the box would by default (can be overruled) have been truncated so that they are confined within the bounding box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Technical details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section provides some more detail on the above demonstrated functionality and may be skipped for users with limited interest in technicalities. We focus on the the technicalities related to 3d meshing; in 2d, meshing is relatively simple (still difficult, but 3d is much worse). \n",
    "\n",
    "Functionality for fractures and their intersections is provided in the subpackage `porepy.fracs`. In addition to the above method of defining fractures through their vertices, we can also specify the fracture as an ellipsis, approximated as a polygon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the fracture center\n",
    "center = np.array([0.1, 0.3, 0.2])\n",
    "# The minor and major axis\n",
    "major_axis = 1.5\n",
    "minor_axis = 0.5\n",
    "\n",
    "# Rotate the major axis around the center.\n",
    "# Note that the angle is measured in radians\n",
    "major_axis_angle = np.pi/6\n",
    "\n",
    "# So far, the fracture is located in the xy-plane. To define the incline, specify the strike angle, and the dip angle.\n",
    "# Note that the dip rotation is carried out after the major_axis rotation (recall rotations are non-commutative).\n",
    "strike_angle = -np.pi/3\n",
    "dip_angle = -np.pi/3\n",
    "\n",
    "# Finally, the number of points used to approximate the ellipsis.\n",
    "# This is the only optional parameter; if not specified, 16 points will be used.\n",
    "num_pt = 12\n",
    "f_3 = pp.create_elliptic_fracture(center, major_axis, minor_axis, major_axis_angle, strike_angle, dip_angle, num_points=num_pt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fractures can be joined into a network in the same way as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = pp.create_fracture_network([f_1, f_2, f_3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have not yet set a boundary for the fracture network, and effectively for\n",
    "the domain. In the previous examples the bounding box was given on instantiation of the fracture network. However, it can also be imposed at a later point, in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define a domain based on its bounding box\n",
    "bounding_box = {'xmin': -2, 'xmax': 3, 'ymin': -2, 'ymax': 3, 'zmin': -3, 'zmax': 3}\n",
    "domain = pp.Domain(bounding_box)\n",
    "_ = network.impose_external_boundary(domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meshing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our aim is to create a computational mesh that conforms to the fractures and their intersections (1d lines, 0d points). For the actual grid construction, we rely on Gmsh. However, Gmsh requires that the geometric constraints, that is the fractures, are described as *non-intersecting* polygons. It only takes some thinking to understand why the meshing software would not like to do this themselves; this is a highly challenging task.\n",
    "\n",
    "PorePy provides functionality for finding intersections between fractures, and splitting them into non-intersecting polygons. Intersections are found by "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.split_intersections()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometric tolerances and stability of meshing algorithm\n",
    "A critical concept in meshing of fractured domains is the concept of geometric tolerance: Since we are operating in finite precision aritmethics, two points may or may not be consider equal (or similarly, two lines/planes may or may not intersect), depending on how accurately we consider their representation. At least three concepts come into play here:\n",
    "\n",
    "1. The accuracy of the numerical representation of the objects (accumulated effect of finite precision rounding errors).\n",
    "2. The accuracy in geological interpretation of fracture data. As an example, if the fracture network originates from an interpretation of satellite images, differences measured in centimeters should be treated with some caution.\n",
    "3. The resolution of the computational grid: If points with a certain distance are considered non-equal, this may also require that we resolve their difference in the mesh. In addition, the mesh generator will use its own concept of geometric tolerance for internal calculations.\n",
    "\n",
    "In PorePy, we attempt to resolve these issues as follows: The `FractureNetwork3d` (and the corresponding `FractureNetwork2d`) has an attribute `tol` that represents the geometric tolerance used in the calculation of intersections and subsequent splitting of the fractures. If meshing is done with gmsh, the tolerances used in PorePy and gmsh are related. The approach works reasonably well, but for complex configurations of fracture intersections, stability issues can arise. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interaction with gmsh\n",
    "\n",
    "Now, we want to create grids for the domain, as well as for fractures and fracture intersections. This involves creating a config file for the mesh generator that contains geometry description, including fracture planes and their intersections. The mesh is then created by calling gmsh. The resulting mesh information is read back to python, and `Grid` objects representing the matrix, fractures and fracture intersections are created.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gmsh is quite flexible in terms of letting the user set/guide the preferred mesh size in different parts of the domain. PorePy tries to adjust to this by adapting the specified mesh size to the geometry. As we have seen previously, the user can specify the mesh sizes by tuning the parameters `cell_size`, `cell_size_min`, `cell_size_fracture` and `cell_size_boundary`. What actually happens with the mesh, that is, how Gmsh translates these preferred options into a grid, is another matter. It may take some practice to get this to work properly.\n",
    "\n",
    "If an acceptable mesh cannot be generated by tuning these parameters, more fine-tuned mesh generation can be achieved directly in Gmsh. A typical workflow would be first to generate a gmsh configuration file using the function `FractureNetwork.prepare_for_gmsh()`, then generate the mesh from Gmsh proper and store it to a .msh file, and finally import the mesh using the functions from `pp.fracture_importer` to obtain a `MixedDimensionalGrid`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What we have explored\n",
    "Fractures in PorePy are defined as convex planes (in 3d) or lines (in 2d). A `FractureNetwork` is a domain that contains a set of fractures, and it is constructed by invoking the method `create_fracture_network()`. A mixed-dimensional mesh is constructed by invoking the method `create_mdg()`. The actual mesh is constructed by the external package Gmsh."
   ]
  }
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